The World Health Organization estimates that 135 million people have diabetes mellitus worldwide and that the number of people with diabetes will increase to 300 million by the year 2025. More than 18 million Americans currently have diabetes and the number of adults with the disease is projected to more than double by the year 2050. An additional 16 million adults between the ages of 40-74 have pre-diabetes and are at an elevated risk for developing diabetes. Visual disability and blindness have a profound socioeconomic impact upon the diabetic population and diabetic retinopathy (DR) is the leading cause of new blindness in working-age adults in the industrialized world. The prevalence rates for DR and vision-threatening DR in adults over age 40 is 40.3% and 8.2%, respectively. It is estimated that as much as $167 million dollars and 71,000-85,000 sight-years could be saved annually in the US alone with improved screening methods for diagnosing diabetic retinopathy.
Most current methods used to address screening for DR rely on either a patient visit to a physician specifically trained to diagnose eye disease from digital retinal photography, or the use of established retinal reading centers such as the Joslin Vision Network (Boston, Mass.), and Inoveon Corp. (Oklahoma City, Okla.). While reading centers have shown that digital photography is an effective tool for identifying DR when performed by experienced, certified readers, the turn-around time for a diagnosis is roughly 72 hours (3 days) on average.
Recently, research has been conducted to develop automated techniques to analyze retinal images for diagnosing some diseases having retinal manifestations. While these automated disease diagnosis techniques have shown promise, even the best still misdiagnose such diseases between 5 and 20% of the time. Whether the misdiagnosis is a false positive or a false negative, the risk of such an elevated error rate limits the commercial usefulness of current automated diagnosis techniques.